Method and system for enhanced search term suggestion

ABSTRACT

Method, system, and programs for providing enhanced search term suggestions. A set of incomplete search terms indicating a sequence of search terms entered may be received. It may be detected that the sequence contains a descending phase followed by an ascending phase. In response to the detection, a pair of misinput term and corresponding corrected term may be identified in the set of incomplete search terms. A probability with respect to the misinput term is a misinput of the corresponding corrected term may be determined based on a historical context. Using such a probability, an incomplete search term containing the misinput term may be corrected. One or more proposed search terms may be determined based on the corrected incomplete search term for suggestion to the user.

BACKGROUND

1. Technical Field

The present teaching relates to methods, systems and programming forprocessing user search inquiries. Particularly, the present teaching isdirected to methods, systems, and programming for suggesting searchterm(s) to a user.

2. Discussion of Technical Background

The advancement in the world of the Internet has made it possible tomake a tremendous amount of information accessible to users locatedanywhere in the world. A search engine is a computer system orapplication that helps a user to locate the information. Using a searchengine, a user can execute a search via a search term to obtain a listof information (i.e., search results) that matches the search term.While search engines may be applied in a variety of contexts, searchengines are especially useful for locating resources that are accessiblethrough the Internet.

Some search engines order the list of matching information beforepresenting the list to a user. For achieving this, a search engine maybe configured to assign a rank to the matching information in the list.When the list is sorted by rank, matching information with a relativelyhigher rank may be placed closer to the head of the list than othermatching information with relatively lower ranks. The user, whenpresented with the sorted list, sees the most highly ranked matchinginformation first. To aid the user in his/her search, a search enginemay rank the matching information according to relevance. Relevance is ameasure of how closely the subject matter of particular informationmatches a search term.

In a typical situation, the user is enabled to enter an intended searchterm from a client computing platform associated with the user (e.g.,smartphone, tablet, laptop, desktop, or any other client computingplatform) via a user interface. Once the user completes inputting theintended search term, the completed search may be transmitted, over acommunications network such as the Internet, to the search engine forexecution. The user interface typically comprises an input box thatallows the user to enter the intended search term one letter at a time.

Conventional search term suggestion techniques for determining andsuggest proposed search term(s) to a user when the user is in progressof entering an intended search term typically employ a database to storehistorical search terms completed by a particular user and/or searchterms completed by users that are “similar” to that particular user. Asthe particular user is in progress of entering an intended search term,these conventional techniques search the database for candidate termsthat may be suggested to the user based on their relevance to theincomplete intended search term entered by the particular user thus far.

In entering the intended search term, the user, however, may not alwayscorrectly input letters into the intended search term. For example,mistyping of the search term might happen when the user incorrectlyinputs some letters into the intended search term. For instance, theuser may misinput (skip, unnecessarily add, and/or wrongly input) one ormore letters in the intended search term. In another example, the usermay modify the intended search term to correct grammar, to have a moreprecise meaning, to change to another search term and/or for any otherconcerns.

Therefore, there is at least a need to account for situations when asearch term as being input may be incorrect when determining proposedsearch term(s) to be suggested to the user.

Therefore, there is a least a need to detect an incomplete search termas being input by a user contains misinput letters or characters whendetermining proposed search term(s) to be suggested to the user becausethe incomplete search term may not be the search term intended by theuser.

Therefore, there is a least a need to establish storage to storeinformation indicating input sequences of search terms by a user areincorrect for facilitating determining proposed search term(s) to besuggested to the user.

Therefore, there is a least a need to enable detection of misinputsearch term as being input a user.

SUMMARY

The teachings disclosed herein relate to methods, systems, andprogramming for processing user search inquiries. More particularly, thepresent teaching relates to methods, systems, and programming fordetermining proposed search term(s) to be suggested to the user based oninput sequence of search terms entered by the user.

In one example, a method, implemented on a machine having at least oneprocessor, storage, and a communication platform connected to a network,for building sequences of search terms in association with correspondingprobability of misinput. By this method, a set of incomplete searchterms may be first received. The received incomplete search terms maycorrespond to a sequence of search term entered by a user. It may thenbe detected in the sequence there is a descending phase followed by anascending phase. Such detection may reveal that the search term has beenmisinput and has been corrected by the user. In response to thedetection, a pair of misinput term and corresponding corrected term maybe identified in the set of incomplete search terms. A probability withrespect to the misinput term is a misinput of the correspondingcorrected term may be determined based on occurrences of these terms ina historical context. In one example, such a probability may bedetermined using a noisy channel model. The probability may then bestored in association with the pair in storage for future use.

In another example, a method, implemented on a machine having at leastone processor, storage, and a communication platform connected to anetwork for enhanced search term suggestion is disclosed. In thismethod, an incomplete search term as being input by a user may bereceived. Storage of sequences of search terms entered by the userand/or other users historically may be consulted for determining whetherthe received incomplete search term is misinput. In one example, anentry of the database may indicate an incomplete term in the receivedincomplete search term has a probability to mean the user actuallyintended a corresponding term. In that example, based on suchprobability or probabilities, an incomplete search term may becorrected. One or more proposed search terms (e.g., complete searchterm, but not necessarily limited to complete search term) may bedetermined based on the corrected incomplete search term for suggestionto the user.

Other concepts relate to software for implementing the enhanced searchterm suggestions. A software product, in accord with this concept,includes at least one machine-readable non-transitory medium andinformation carried by the medium. The information carried by the mediummay be executable program code data regarding parameters in associationwith a request or operational parameters, such as information related toa user, a request, or a social group, etc.

In one example, a machine readable and non-transitory medium havinginformation recorded thereon for making enhanced search termsuggestions, where when the information is read by the machine, causesthe machine to receive a set of incomplete search terms corresponding toa sequence of a search term entered by a user; detect in the sequence adescending phase followed by an ascending phase indicating that at leastone search term in the set of incomplete search terms has beencorrected; identify, in the set of incomplete search terms, a pair of amisinput term and a corresponding corrected term, in response to thedetection; determine a probability with respect to the misinput term isa misinput of the corresponding corrected term; and store the pair withthe probability for future use.

Additional advantages and novel features will be set forth in part inthe description which follows, and in part will become apparent to thoseskilled in the art upon examination of the following and theaccompanying drawings or may be learned by production or operation ofthe examples. The advantages of the present teachings may be realizedand attained by practice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIGS. 1A-1C illustrate a high level depiction of exemplary systems inwhich enhanced search term suggestion is applied in accordance with thepresent teaching;

FIG. 2 is a flowchart of an exemplary process of determining anincomplete search term is misinput by a user in accordance with oneexample of the disclosure;

FIG. 3 illustrates one example of incomplete search terms that may beentered by a user for inquiring about related information;

FIG. 4 is a high level depiction of an example of search term suggestionengine shown in FIG. 1;

FIG. 5 illustrates another example of the search suggestion engine shownin FIG. 1;

FIG. 6 illustrates an example of search term misinput detection unitshown in FIG. 5;

FIG. 7 illustrates an example of the misinput detection criteria thatmay be used to identify a pair of incomplete search terms represent amisinput term and a corresponding corrected term;

FIG. 8 conceptually illustrates an edit distance between two terms;

FIG. 9 conceptually illustrates the edit distance criteria shown in FIG.8 may be used to determine whether a pair of terms represents misinputterm and corrected terms;

FIG. 10 conceptually illustrates an example of approximation criteriabeing used to identify the misinput term and the corresponding correctedterm;

FIG. 11 illustrates an example of misinput/correction pair identifiershown in FIG. 6;

FIG. 12 conceptually illustrates one example of extracting n-grams fromthe misinput/correction pair illustrated in FIG. 10;

FIG. 13 illustrates an example of N-Grams extractor shown in FIG. 5;

FIG. 14 illustrates an example of N-gram misinput probability analyzershown in FIG. 5;

FIG. 15 illustrates an example storing probability determined in FIG. 14for a given pair of n-grams in association with the given n-grams in acorrection database;

FIGS. 16A-B illustrate an exemplary method of storing N-gram pair in acorrection database in accordance with the one embodiment of thedisclosure;

FIG. 17 illustrates one example of the search term processing unit 510shown in FIG. 5;

FIG. 18 depicts architecture of a mobile device which can be used toimplement a specialized system incorporating the present teaching; and

FIG. 19 depicts architecture of a computer which can be used toimplement a specialized system incorporating the present teaching.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The present teaching relates to systems, methods, medium, and otherimplementations directed to enhancing search term suggestion based oncorrected misinput in a set of incomplete search terms realized as aspecialized and networked system by utilizing one or more computingdevices (e.g., mobile phone, personal computer, etc.) and networkcommunications (wired or wireless). The disclosed teaching on enhancedsearch term suggestion includes, but not limited to, an online processand system that in situations where a user may enter search terms from aclient computing platform associated with the user. The progress of theuser entering the search term may be monitored by recording theincomplete search terms as being input by the user towards thecorresponding complete search term. Such incomplete search terms mayreveal a sequence of different versions of a search term entered by theuser with, e.g., the final version being the search term actuallyintended by the user. Such a sequence of different versions of a searchterm entered by the user may be recorded and leveraged for improvingsearch term suggestions.

Based on the sequence of a search term as entered by a user, it may beidentified whether the sequence contains a descending phase followed byan ascending phase. In cases where such phases are detected, a pair ofmisinput search term and corresponding corrected search term may beidentified from the set of incomplete search terms. Intuitively, thecorresponding corrected search term is what is intended by the user andthe misinput in the pair is a misinput of what is intended. According tothe present teaching, to facilitate improved search suggestion, aprobability that the misinput term is indeed a misinput of thecorresponding corrected term may be computed based on occurrences ofthese terms in a historical context. The probability may then be storedin association with the pair, e.g., as an entry in a database, as aprobabilistic suggestion alternative for a future misinput as recordedbased on the corresponding corrected search term as stored. To utilizethe stored probabilistic suggestion alternatives to make a search termsuggestion, incomplete search terms entered by a user during a futuresession may be monitored while the system is in consultation with thedatabase. When a misinput term is detected entered by the user, thestored corresponding corrected search term may be retrieved and used toreplace the misinput term to automatically correct the misinput usingthe stored corrected incomplete search term. When there are multiplepairs corresponding to the same misinput, each with a separateprobability, the system implemented according to the present teachingmay select a suggestion that has the highest probability.

As used herein, an intended search term may be referred to as a searchterm intended by a user for a search engine to execute and to return alist of information matching the search term.

As used herein, an incomplete search term may be referred to as a searchterm that is partially input by a user. As such, an incomplete searchterm may or may not constitute a part of the intended search termactually meant by the user. For example, there are situations in whichthe user may misinput (e.g., skip, unnecessarily add, and/or use wrongletter/characters) when entering an incomplete search term.

As used herein, an input sequence of an incomplete search term by a usermay be referred to as a sequence of letters while the user entering theincomplete search term. It should be appreciated that a user may enteran incomplete search term using any suitable input means, such as, butnot limited to, key strokes enabled by a physical keyboard, fingertapping enabled by a virtual keyboard, finger swiping enabled by a touchpad, voice commands enabled by a voice recognition service, styluswriting enabled by a touch pad, and/or any other input means. It shouldalso be appreciated that the input sequence of an incomplete search termmay not necessarily be limited to one letter at a time. For example, itis understood that an input sequence by, e.g., swipe typing or suggestedtyping may be used by a user to input multiple letters into anincomplete search term at a time. It is also understood that, althoughvarious examples illustrated in this disclosure are English based searchterms, the present teaching is not limited to English based searchterms. For example, the present teaching may be applied to an inputsequence of an incomplete search term in any language, such as Spanish,German, French, Chinese, Korean, Japanese, Greek, Latin, and Hindi. Thepresent teaching is also not limited to linguistically meaningful inputand may include any commonly known meaningful sequence of symbols, suchas math symbols, chemistry symbols, and/or any other types of inputs ofletters, alphabets or characters, and numerals that may be used in humancommunications.

As used herein, the terms “letter”, “alphabet”, “character” may be usedinterchangeably in the context of a search term to mean a singularconstituting part of a search term.

FIGS. 1A-1C illustrate exemplary system configurations in which enhancedsearch term suggestion can be deployed in accordance with variousembodiments of the present teaching. In FIG. 1A, the exemplary system100, as shown, includes users 110, a network 120, a search engine 130,content sources 160, external resource(s) 150, content sources 160 andother components (if any). The network 120 in system 100 can be a singlenetwork or a combination of different networks. For example, a networkcan be a local area network (LAN), a wide area network (WAN), a publicnetwork, a private network, a proprietary network, a Public TelephoneSwitched Network (PSTN), the Internet, a wireless network, a virtualnetwork, or any combination thereof. A network may also include variousnetwork access points, e.g., wired or wireless access points such asbase stations or Internet exchange points 120-a, . . . , 120-b, throughwhich a data source may connect to the network in order to transmitinformation via the network.

Users 110 may be of different inputs such as users connected to thenetwork via desktop connections (110-d), users connecting to the networkvia wireless connections such as through a laptop (110-c), a handhelddevice (110-a), or a built-in device in a motor vehicle (110-b). A usermay send a query to the search engine 130 via network 120 and receive aquery result from the search engine 130 through network 120. Based onthe query received from the user, as illustrated in FIG. 1A, search termsuggestions may be returned to the user to aid the user to complete orfine tune the query.

The exemplary system 100 as shown in FIG. 1A includes a search engine130, which may include various components including a search termsuggestion unit 134. As illustrated in FIG. 1A, these components in thesearch engine 130 may operate and communication with each other via abus or buses included in the search engine 130. The search term unit 134may be configured to provide enhanced search term suggestion inaccordance with the present teaching. It should be understood thearchitecture with respect to providing enhanced search term suggestionin accordance with the present teaching is not limited to that shown inFIG. 1A. For example, FIG. 1B illustrates another architecture by whichenhanced search term suggestion in accordance with the present teachingmay be provided. As shown in FIG. 1B, the functionality attributed tosearch term unit 134 may be provided by a search term suggestion engine140, which may be discrete and separate from the search engine 130 asshown. As illustrated, the search term suggestion engine 140 may beconnected to the search engine via network 130. In one example, thesearch engine 130 may employ search term suggestion engine 140 byforwarding search terms to the search term suggestion engine 140 andreceiving search term suggestions from the search term suggestion engine140. FIG. 1C illustrates yet another architecture by which enhancedsearch term suggestion in accordance with the present teaching may beprovided. As shown in FIG. 1C, the search term suggestion engine 140 maybe operatively connected to the search engine 130 via a suitablecommunication channel. For example, the search term suggestion engine140 and the search engine 130, as shown in FIG. 1C, may be located inthe same server rack cabinet, or the same server room.

The external resources 150 may include sources of information, hostsand/or providers of Internet services outside of system 100, externalentities participating with system 100, and/or other resources. In someimplementations, some or all of the functionality attributed herein toexternal resources 150 may be provided by resources included in system100. Examples of external resources may include data resources providedby third party content providers, Internet services provided by thirdparty internet service providers, advertisement servers, and/or anyother inputs of resources provided by participants external to system100.

The content sources 160 may include multiple content sources 160-a,160-b, . . . , 160-c. A given content source 160 may correspond to a webpage host corresponding to an entity, whether an individual, a business,or an organization such as USPTO.gov, a content provider such as cnn.comand Yahoo.com, or a content feed source such as tweeter or blogs. Thesearch engine 130 may access information from any of the content sources160-a, 160-b, . . . , 160-c and rely on such information to respond to aquery (e.g., the search engine 130 identifies content related tokeywords in the query and returns the result to a user). Similarly, thesearch term suggestion engine 140 may access additional information, vianetwork 120.

In the exemplary system 100 shown in FIGS. 1A-C, a user may inquireabout certain information by entering search term for a client computingplatform such as 110-a to 110 d. For example, the user may inquire about“what is the best Oscar movie 2014” by entering a search term indicatingsuch one or multiple characters/letters at a time from a clientcomputing platform 110. The search term suggestion unit 134 (FIG. 1A) orthe search term suggestion engine 140 (FIGS. 1B-C) may receive a set ofincomplete search terms as the user enters the search term. The set ofincomplete search terms may reveal different versions of the search termactually intended by the user. Based on the incomplete search termsreceived, the search term suggestion unit 134 or the search termsuggestion engine 140 may determine search suggestions for presentationon the user client computing platform 110.

FIG. 2 is a flowchart of an exemplary process of determining anincomplete search term is misinput by a user in accordance with oneexample of the disclosure. It will be described with reference to FIGS.1A-C. As shown, at 210, a set of incomplete search terms may bereceived. The incomplete search terms may indicate search termspartially entered by the user for inquiring about related information.For example, the incomplete search terms may be partially entered by theuser before the user engages an inquiry represented by the search term(complete)—for example, by hitting the “search button” provided by agraphical user interface implemented on the client computing platform110. In some implementations, a client program module such as a userinput monitor (e.g., keystroke monitor) may be installed on the clientcomputing platform to monitor user progress of entering a search term(character(s)/letter(s) by character(s)/letter(s)). In thoseimplementations, a progress of user entering the search term may bereported to the search engine 130 (FIG. 1A) and/or the search termsuggestion engine 140 (FIGS. 1B-C). By way of non-limiting example,individual ones of the letters in the search term “what is the bestOscar movie 2014” may be reported to the search engine 130 and/or thesearch term suggestion engine 140 sequentially as they are entered bythe user on the client computing platform. However, it should beappreciated that the incomplete search term may be received in someother ways that are not necessarily “real-time” as described above. Forexample, the incomplete search terms may be received from a database inwhich historical search terms may be stored in association withcorresponding users. Such a database may store the historical searchterms in a “replay” format to reflect how the search terms were enteredby the users. For example, if a user entered a search term with 3backspaces to erase a typo, the 3 backspaces may be captured in thedatabase in the “replay format” such that the entering of the searchterm by the user may be “replayed” for analysis, for example inaccordance with the present teaching.

FIG. 3 illustrates one example of incomplete search terms that may beentered by a user for inquiring about related information. It will bedescribed with reference to FIGS. 1A-C and FIG. 2. As shown, the user110 may enter a set of search terms 310 for inquiring about “what is thebest Oscar movie 2014”. A given one of the search terms 310 as shown mayindicate a partially entered search term input by the user at acorresponding time point. As described above, the incomplete searchterms 310 may be received at step 210.

Returning to FIG. 2, at 220, a descending phase followed by an ascendingphase may be detected in the search terms received at 210. Inimplementations, for such detection, the incomplete search termsreceived at 210 may be compared with one another. Referring to FIG. 3,as shown, as the length of the incomplete search terms received grows, afirst ascending phase may be indicated, which may mean the user isadding letter or character to the search term. When the length of theincomplete search terms starts reducing, a descending phase may bedetected, which may mean the user is backspacing (e.g., erasing)previously input letter(s)/character(s). A second ascending phase may bedetected as the length of the search terms starts growing again afterthe detection of the descending phase, which may mean the user hasfinished erasing misinput characters/letters and starts addingletter(s)/character(s) in place of the erased ones and as well as newones to complete the search term.

Returning to FIG. 2, at 230, a pair of misinput term and correspondingcorrected term may be determined from the set of search terms receivedat step 210, in response to the detection at step 220. This is alsoillustrated in FIG. 3. As shown in FIG. 3, the incomplete search term atthe beginning of the descending phase, i.e., the incomplete search term310 a, may be obtained as the misinput term, and the incomplete term 310b in the ascending phase may be obtained as the corresponding correctedterm. In one implementation, the term 310 b may be obtained in thesecond ascending phase because it has the same length as the term 310 a.However, it should be understood this is not necessarily the only case.In some other examples, criteria other than identical length may be usedto obtain the corresponding corrected term 310 b and will be describedbelow.

Returning to FIG. 2, at 240, an n-gram pair may be extracted from thesearch term pair determined at step 230. As used herein, n-gram may bereferred to as n number of letters/characters in a search term.Accordingly, at step 240, an n (e.g., 6) letter long “gram” may beextracted from the misinput term 310 a and corresponding n letter long“gram” may be extracted from the corresponding corrected term 310 b.

At 250, a probability of the n-gram pair indicates a future occurrenceof the first n-gram (in the pair) in a search term entered by a user maymean the user actually intends to enter the second n-gram (in the pair).Various statistical models may be applied to determine such aprobability. For example, in one implementation, a noisy channel modelmay be used to determine whether the first n-gram in the pair extractedat 240 is indeed a misinput of the second n-gram in that pair. To usethe noisy channel model, historical search terms entered by the userand/or other users may be obtained, for example from a database ofsearch terms captured in “replay” format as described above. With suchinformation, the number of times the first n-gram was input by the userhistorically without and with changing to the second n-gram may beobtained, respectively. With this information, a probability that thefirst n-gram in the pair when entered by the user in the future actuallymeans the second n-gram in the pair may be determined.

At 260, the probability determined at step 250 may be stored inassociation with the n-gram pair extracted at step 240. For example, then-gram pair along with the probability may be saved as an entry in acorrection database. The correction database having such entries may beused to correct incomplete search terms for making search suggestions inthe future.

FIG. 4 is a high level depiction of an example of search term suggestionengine 140 shown in FIG. 1. As shown, the search term suggestion engine140 may be configured to receive historical search terms input by a userof interest and/or other users similar to that user. For example,without limitation, the historical search terms may include search termsentered by the user in the past 30 days, and/or by users identified tobe similar to the user (e.g., having similar age, similar interests,similar region of residence, and any other similarity). The historicalsearch terms, as described above, may be in a “replay” format capturingexactly how the search terms were entered by the user or users includingbackspaces. As also shown, the search term suggestion engine 140 may beconfigured to generate updates to correction information (correctiondatabase) as described above. For example, using the historical searchterms, the search term suggestion engine 140 may obtain n-gram pairs andassociated probabilities of misinput as indicated by the n-gram pair,and stored the n-gram pairs and the associated probabilities in acorrection database as search term correction information. As stillshown, the search term suggestion engine may be configured to receivethe search term correction information and a current incomplete searchterm. Based on the search term correction information (e.g., a firstn-gram has 90% of chance actually means a second n-gram if the firstn-gram appears in the current incomplete search term), the search termsuggestion engine may make a correction to the current incomplete searchterm and make search term suggestions based on the corrected incompletesearch term.

FIG. 5 illustrates another example of the search suggestion engine 140shown in FIG. 1. As shown in this example, the search term suggestionengine 140 may include a search term processing unit 510, a search termmisinput detection unit 520, an n-grams extractor 540, an n-grammisinput probability analyzer 530 and/or any other components. Thesearch term misinput detection unit 520 may be configured to receivehistorical search terms 580, current incomplete search term as beingentered by user, and/or any other search term. The search term misinputdetection unit 520 may be configured to obtain control output indicatingthat a descending phase followed by an ascending phase is detected in agiven set of search terms received by the search term misinput detectionunit 520. As illustrated, the search term misinput detection unit 520may be configured to determine and forward misinput/correction termpairs (e.g., terms 310 a and 310 b illustrated in FIG. 3) to the n-gramsextractor 540. The determination of the misinput/correction term pairsby the search term misinput detection unit 520 may be based on themisinput detection criteria 570, which may be dynamically configured. Asstill shown, the n-grams extractor 540 may be configured to extract apair of n-grams from the terms of interest received from the search termmisinput detection unit 520. The N-grams extractor 540 may be configuredto forward the extracted n-gram pairs (e.g., n-gram of interest as shownin FIG. 5) to the n-gram misinput probability analyzer 530 for adetermination of probability that n-gram pair indicates misinput. Asshown, the n-gram misinput probability analyzer 530 may be configured toreceive historical search terms and determine the probability that then-gram pair received from the n-gram extractor 540 indicates misinput bythe user. The n-gram misinput probability analyzer 530 may be configuredto return the determined probability to the N-grams extractor 540, whichmay be configured to store the extracted n-gram pair in a correctiondatabase 560 as shown. The search term processing unit 510 may beconfigured to receive current incomplete search terms as being enteredby a user, search term correction information (e.g., a probabilityindicating misinput a second n-gram is misinput as a first n-gram), andhistorical search terms by the user and/or similar users. Based on thereceived information, the search term processing unit 510 may beconfigured to correct the incomplete search term and make search termsuggestions based on the corrected search term.

FIG. 6 illustrates an example of search term misinput detection unit 520shown in FIG. 5. It will be described with reference to FIG. 5. Asshown, the search term misinput detection unit 520 may include acontroller 610 and a misinput pair identifier 620. The controller 610may be configured to receive a set of incomplete search terms, e.g., thehistorical search terms 580 and/or the current incomplete search termsshown in FIG. 5. As illustrated, the controller 610 may be configured toforward the received terms to the misinput pair identifier 620 foridentifying a descending phase followed by an ascending phase in thesequence indicated by the incomplete search terms. As shown, theidentification by the misinput pair identifier 620 may be based onmisinput detection criteria, such as the misinput detection criteria 570shown in FIG. 5.

FIG. 7 illustrates an example of the misinput detection criteria thatmay be used to identify a pair of incomplete search terms representing amisinput term and a corresponding corrected term. As shown in thisexample, the misinput detection criteria may include an edit distancecriteria, which is conceptually illustrated in FIG. 8. As shown, a setof incomplete search terms 810 may be received. The set of incompletesearch terms may correspond to a sequence of user entering a search termfrom a client computing platform. As discussed above, an incompleteterm, such as the incomplete search term 810 a shown, may be identifiedas the misinput term because it is at the beginning of the descendingphase in the sequence; and a corresponding corrected term, such as thecorrected term 810 b, may be identified because it has the same lengthas the misinput term 810 a and it is in the ascending phase followingthe descending phase as shown. As illustrated, the edit distance betweenthe terms 810 a and 810 b may be a minimum number of edit operations,e.g., single backspace as shown in FIG. 9, required to transform term810 a to term 810 b.

FIG. 9 illustrates, conceptually, the edit distance between two termsshown in FIG. 8 may be used to identify whether the terms representing amisinput term and corresponding corrected term. As shown, a thresholdvalue may be used to determine whether an edit distance between twoterms—e.g., the term at the beginning of the descending phase and theterm that has the same length in the ascending phase—has breached thethreshold value. This threshold value may be used to reducemisidentifying two terms when they do not represent misinput andcorresponding correction. Empirically, edit distance greater thancertain value such as 4 to 6 typically indicates a user is erasingpreviously input characters/letters to fine tune the search term insteadof correcting mistyping. In this example, an edit distance threshold of10 is used. As illustrated, the edit distance between the pair “what osthe” and the corresponding “what is the” is less than 10 in thisexample, and thus may be used to indicate that this pair of termsrepresents a misinput and correction pair. By contrast, the editdistance between the pair “Filming three kings” and “Filming Nebraskamo” has breached the threshold value 10, and thus may not represent amisinput term and corresponding correction term.

Returning to FIG. 7, as shown, the misinput detection criteria 710 mayinclude approximation criteria. FIG. 10 conceptually illustrates anexample of approximation criteria that may be used to identify themisinput term and the corresponding corrected term. As shown in FIG. 10,a set of incomplete search terms may indicate the user has erased someletters in the search term and add new letter(s) to the search term. Toaccount for situations where the user erased less letters than lettersadded to the search term, the same length criteria discussed above maybe augmented as approximation range criteria. By way illustration, inFIG. 10, the misinput term is “what_os_the” because it is at thebeginning of the descending phase. The incomplete search term“what_was_th” as shown would be the corrected term if the same lengthcriteria were used as discussed above because it is located in theascending phase (following the descending phase) and it has the samenumber of character(s)/letter(s) as the misinput term 1110 a. However,as can be seen in FIG. 10, the best match for the corrected term is not“what_was_th”, but rather “what_was_the”. This is due to after erasingthe typo “os”, the user added “was” to replace “os”. Since “was” has onemore letter than “os”, the resulting new search term is longer than theold search term. Accordingly, solely relying on the same length criteriais not enough in this case. To address this, the approximation rangecriteria may be introduced such that a number of incomplete search termsaround “what_was_th” may be examined for determining which one may bethe best match. In one example, semantic examination may be used to findthe best match among the number of incomplete search terms. Forinstance, the individual incomplete search terms may be examined todetermine a number of semantic words contained in the incomplete searchterm, and the one with most semantic words and closest to the incompletesearch term that has the same length as the misinput term may bedetermined as the best match for the corrected term corresponding to themisinput term 1010 a.

FIG. 11 illustrates an example of misinput/correction pair identifier620 shown in FIG. 6. As shown, the misinput/correction pair identifier620 may be configured to include a phase identifier 1110, a misinputterm detector 1120, a candidate corrected term extractor 1130, and/orany other components. The phase identifier 1110 may be configured todetermine phases in a sequence represented by a set of incomplete terms.The phases that may be determined by the phase identifier 1110 mayinclude an original (first) ascending phase, a first descending phase, asecond descending phase immediately following the first descendingphase, a second ascending phase immediately following the seconddescending phase, and/or any other phases. As illustrated, the phaseidentifier 1110 may forward such phase information to misinput termdetector 1120, which may be configured to determine a misinput term inthe sequence. As discussed above, in one example, the misinput detector1120 may be configured to obtain the first incomplete term at thebeginning of a descending phase as the misinput term. As stillillustrated, the misinput term determinator 1120 may be configured togenerate control information instructing the candidate corrected termextractor 1130 to obtain a corrected term corresponding to the misinputterm. As shown, the candidate corrected term extractor 1130 may beconfigured to obtain the corrected term based on received approximationrange criteria as (illustrated in FIG. 10), an edit distance criteria(illustrated in FIG. 11) and/or any other criteria.

N-gram is a text processing concept. In some implementations, n-grampairs may be extracted from the misinput/corrected term pair acquired bythe misinput/correction pair identifier 520. For example, a portion ofcharacters/letters in the pair may be extracted as n-grams. This mayimprove efficiency for future processing. The idea is that only aportion of the misinput and correction pair acquired by themisinput/correction pair identifier 520 may be relevant for correctingany future search terms, therefore only this portion may be stored. Thisalso improves the processing efficiency because less data would beprocessed in the n-gram case than the misinput/correction pair case.

FIG. 12 conceptually illustrates one example of extracting n-grams fromthe misinput/correction pair illustrated in FIG. 10. As shown, an n-gram1210 “at_os_” may be extracted from the misinput term “What_os_the”.Rules that may be used to extract the n-gram may vary. For example, thedesired n-gram may be extracted around the letter when the backspacingwas stopped, e.g., 2 letters before and 2 letters after. The length ofthe n-gram to be extracted may also be however desired. In experiments,the length “6” appears to be optimal in achieving a balance betweenaccuracy and efficiency. However, it should be understood this is notmeant to be limiting. One skilled in the art would understand the lengthof an n-gram n may be any reasonable number determined based onapplication needs. As also shown in FIG. 12, the n-gram 1220 “at_is_”may similarly be extracted from the corrected term “What_is_the”. Thus,the n-gram “at_os_” and “at_is_” may form an n-gram pair.

FIG. 13 illustrates an example of n-grams extractor 540 shown in FIG. 5.It will be described with reference to FIG. 5. As shown, the n-gramsextractor 540 may be configured to receive misinput term and correctedterm, for example from the search term misinput detection unit 520, andreceive extraction criteria, such as the length of the n-gram to beextracted, the position in the term where the extraction should begin,and/or any other criteria for extracting n-grams from the termsreceived. As shown, the n-gram extractor 540 may be configured toextract the n-grams, for example, in a fashion illustrated in FIG. 12.

As discussed above, a probability of the n-grams acquired by the N-gramsextractor 540 may be obtained by the N-gram misinput probabilityanalyzer 530. The probability determined by the N-gram misinputprobability analyzer 530 may be used to indicate likelihood that thefirst n-gram in an n-gram pair (e.g., “at_os_”) is a misinput of thesecond n-gram in the pair (e.g., “at_is_”) when it appears in a futuresearch term entered by a user. As discussed above, n-grams extracted bythe N-grams extractor 540 may be associated with differentprobabilities, as determined by the n-gram misinput probability analyzer530, indicating different likelihood that they represent misinput ofsearch terms in the future. For example, a user may be inclined tomisinput certain words such as “what_is_” to “what_os_” due to habits.In that case, there may be a high probability that “at_os_” actuallymeans “at_is_” when it appears in a search term entered by a user. Bycontrast, the user may misinput “iss” as “is” a few times, but for mostother times, whenever the user inputs “iss” such as in “kiss”, “Swiss”,“dissatisfaction”, the user is always correct without backspacing. Inthat case, the n-gram pair of “at_iss” and “at_is_” would not indicatethat “iss” is a misinput of “is” when it appears in the user search termin the future.

For determining such a probability, various statistical methods may beapplied and configured into the n-gram misinput probability analyzer530. For example, a noisy channel may be configured into n-gram misinputprobability analyzer 530. Noisy channel model is a well-known concept inthe art, and will not be described here in detail. Briefly, to implementthe noisy channel model, the n-gram misinput probability analyzer 530would be configured to receive the following inputs: a number of timesthe first n-gram in an n-gram pair was input by the user withoutcorrection, e.g. P(x); a number of times the second n-gram in the pairwas input by the user without correction, e.g., P(w); a number of timesthe first n-gram was corrected to the second n-gram by the user, e.g.,P(x|w). Using these inputs, the probability of the first n-gram may bemisinput for the second n-gram may be determined by the followingformula: P=P(x|w)P(w)/P(x).

FIG. 14 illustrates an example of N-gram misinput probability analyzer540 shown in FIG. 5. As shown, the n-gram misinput probability analyzer530 in this example is configured to include a historical termoccurrence retriever 1410, an n-gram misinput occurrence retriever 1420,a misinput probability calculator 1430 and/or any other components. Asshown, the historical term occurrence retriever 1410 may be configuredto receive historical search terms from a database, and n-gram pairs ofinterest. Based on a given n-gram pair received, the historical termoccurrence retriever 1410 may extract a number of times the first n-gramin a n-gram pair was input by the user without correction, and a numberof times, the second n-gram in the pair was input by the user withoutcorrection. As still illustrated, the n-gram misinput occurrenceretriever 1420 may be configured to receive the n-gram pairs of interestand historical search terms from the database. Similarly, the n-grammisinput occurrence retriever 1420 may be configured to obtain a numberof times the first n-gram was corrected to the second n-gram by the userusing these inputs. As still illustrated, the misinput probabilitycalculator 1430 may be configured to receive outputs of the historicalterm occurrence retriever 1410 and the n-gram misinput occurrenceretriever 1420 as inputs and implement the formula P(x|w)P(w)/P(x) todetermine a probability that the first n-gram is misinput of the secondn-gram in the pair. As also shown, the probability determined by themisinput probability calculator 1430 may be stored in a correctiondatabase in association with the n-gram pair.

FIG. 15 illustrates an example storing probability determined in FIG. 14for a given pair of n-grams in association with the given n-grams in acorrection database. As shown, the n-gram pairs 1510 may be stored inthe correction database along with the corresponding probability toindicate likelihood the first n-gram of a given pair is a misinput ofthe second n-gram in the given pair. As illustration, an entry may bestored in the correction database to indicate there is 80% of likelihoodthat the first n-gram in the pair—“at_os_” is a misinput of the secondn-gram—“at_is_”.

FIGS. 16A-B illustrate an exemplary method of storing N-gram pair in acorrection database in accordance with the one embodiment of thedisclosure. They will be continuously described with reference to FIG.5. As shown, at 1605, an incomplete search term may be received. In someimplementations, step 1605 may be implemented by a search term misinputdetection unit the same as or substantial similar to the search termmisinput detection unit 520 described herein.

At 1610, a decision may be made whether a backspace mode is detected.For example, the backspace mode may be set on whenever backspacing isdetected by comparing the search term received at step 1605 withpreviously received incomplete search term(s). In some implementations,step 1610 may be implemented by a search term misinput detection unitthe same as or substantial similar to the search term misinput detectionunit 520 described herein. As shown, in the case where the backspacemode is not detected, the method proceeds to step 1615, and in the casewhere the backspace mode is detected, the method proceed to step 1635.

At 1615, a decision may be made whether an ascending immediatelyfollowing a previous descending phase is detected. As described above,such a phase may indicate that the user is correcting the search term.In some implementations, step 1615 may be implemented by a search termmisinput detection unit the same as or substantial similar to the searchterm misinput detection unit 520 described herein. As shown, in the casewhere the ascending immediately following a previous descending phase isnot detected, the method proceeds to step 1620, and in the case wherethe such a mode is detected, the method proceed to step 1660.

At 1620, the incomplete search term may be compared with terms receivedpreviously to detect backspacing. As discussed above, backspace mode maybe turned on when the currently received term has a length smaller thanthe previous received term(s). In some implementations, step 1620 may beimplemented by a search term misinput detection unit the same as orsubstantial similar to the search term misinput detection unit 520described herein.

At 1625, a decision may be made to determine whether backspacing isdetected. As shown, in the case where the backspacing mode is notdetected, the method proceeds to step 1620, and in the case where thebackspace mode is detected, the method proceed to step 1660.

At 1630, the backspace mode may be set to yes, and the incomplete searchterm received at step 1605 may be saved as the misinput term. In someimplementations, step 1630 may be implemented by a search term misinputdetection unit the same as or substantial similar to the search termmisinput detection unit 520 described herein.

At 1635, the incomplete search term received at step 1605 may becompared with terms previously received to detect if backspacing hasstopped. In some implementations, step 1635 may be implemented by asearch term misinput detection unit the same as or substantial similarto the search term misinput detection unit 520 described herein.

At 1640, a decision whether the backspacing has stopped may be madebased on the comparison performed at step 1635. In some implementations,step 1640 may be implemented by a search term misinput detection unitthe same as or substantial similar to the search term misinput detectionunit 520 described herein. As shown, in the case where backspacing isnot stopped, the method proceeds to step 1605, and in the case wherebackspacing is stopped, the method proceed to step 1645.

At 1645, a number of backspaces may be determined as the edit distanceillustrated above and backspace mode may be set to no (since thebackspacing has stopped). In some implementations, 1645 may beimplemented by a search term misinput detection unit the same as orsubstantial similar to the search term misinput detection unit 520described herein.

At 1650, the number of backspaces determined at 1645 may be comparedwith a threshold value. As discussed above, the operation performed at1650 is to account for situations in which the user fine tunes thesearch term instead of correcting the search term. As also discussed,edit distance greater than certain threshold value typically indicatesthe user is fine tuning the search term instead of correcting the searchterm. In some implementations, step 1650 may be implemented by a searchterm misinput detection unit the same as or substantial similar to thesearch term misinput detection unit 520 described herein.

At 1655, the ascending after descending mode may be set to yes. In someimplementations, step 1655 may be implemented by a search term misinputdetection unit the same as or substantial similar to the search termmisinput detection unit 520 described herein.

Now referring to FIG. 16B, at 1660, the search term received at 1605 maybe compared with the misinput term saved at 1630. As discussed above, anumber of criteria may be used for the comparison performed at step1660. For example, the same length criteria may be used such that thecorrected term may be detected if it has the same length as the savemisinput term. As another example, the approximation range criteria maybe used such the corrected term may be detected if it has the mostsematic words among a range of incomplete search terms centered on onethat has the same length as the save misinput term. In someimplementations, 1660 may be implemented by an n-gram extractor the sameas or substantial similar to the search term n-gram extractor 540described herein.

At 1665, a decision may be made whether the corrected term is detectedbased on the criteria described above. In some implementations, step1665 may be implemented by an n-gram extractor the same as orsubstantial similar to the search term n-gram extractor 540 describedherein. As shown, in the case where the corrected term is detected, themethod may proceed to step 1670 and in the case where the corrected termis not detected, the method may proceed back to step 1605.

At 1670, a pair of n-gram may be extracted from the pair of misinputterm (step 1630) and the corrected term (step 1665). As discussed above,the operation performed at step 1670 is to extract a portion of themisinput/correction term pair such that the most relevant part of thepair may be used for future processing. In some implementations, step1670 may be implemented by an n-gram extractor the same as orsubstantial similar to the search term n-gram extractor 540 describedherein.

At 1675, a probability whether the n-gram pair extracted at step 1670may be determined to indicate a likelihood the n-gram pair reflectmisinput of the user that may be predicted in the future. As discussedabove, a number of statistical models may be used for performing theoperation at 1675. Among these models is the noisy channel model. Insome implementations, 1675 may be implemented by an n-gram misinputprobability analyzer the same as or substantial similar to the n-grammisinput probability analyzer 530 described herein.

At 1680, a decision whether the probability determined at step 1675 maybe made. As shown, in the case where the probability is greater than athreshold value, the method may proceed to 1690 to save the extractedn-gram pair along with the probability; and in the case where theprobability is lower than the threshold, the method may proceed to step1685. In some implementations, 1680 may be implemented by an n-gramextractor the same as or substantial similar to the search term n-gramextractor 540 described herein

At 1685, the descending after ascending mode may be set to no and methodmay be caused to proceed back to step 1605. In some implementations,step 1685 may be implemented by an n-gram extractor the same as orsubstantial similar to the search term n-gram extractor 540 describedherein.

Returning to FIG. 5, the search term processing unit 510 as shown may beconfigured to receive current incomplete search term, detect and correctmisinputting of the search term using entries in the correction database560, and make search term suggestions based on corrected search terms.For example, as illustration, an incomplete search term “What_os_the_be. . . ” may be received by the search term processing unit 510. Thesearch term processing unit 510 may be configured to consult thecorrection DB 560 and determine that the “at_os” in the term received islikely a misinput of “at_is”. Based on this determination, the searchprocessing unit 510 may correct (temporary or permanently) the receivedincomplete search term to “What_is_the_be . . . ”. Using otherinformation such as contextual information (which may provide a cluethat the user is search for tea product), user information (which maygive a clue that the user is currently located in China), and historicalinformation (which may indicate the user resides in the US) from thesearch term database 550, the search term processing unit 510 may beconfigured to make search term suggestion“What_is_the_best_tea_in_China”.

FIG. 17 illustrates one example of the search term processing unit 510shown in FIG. 5. As illustrated, the search term processing unit 510 maybe configured to include a term correction unit 1710, a term suggestionunit 1720, and/or any other components. As shown, the term correctionunit 1710 may be configured to receive an incomplete search term andn-gram correction pairs as inputs. Based on the n-gram correction pairs(e.g., received from the correction database 560 shown in FIG. 5, theterm correction unit 1710 may make a correction to a given incompletesearch term received if it detects the incomplete search term containsan n-gram in the n-gram pairs. As shown, the term correction unit 1710may forward the incomplete search term corrected based on the n-grampairs to the term suggestion unit 1720. The term suggestion unit 1720may be configured to make search term suggestions (complete searchterms) based on the corrected incomplete search term forwarded from theterm correction unit 1710.

FIG. 18 depicts architecture of a mobile device which can be used torealize a specialized system implementing the present teaching. In thisexample, the user device on which content and advertisement arepresented and interacted-with is a mobile device 1800, including, but isnot limited to, a smart phone, a tablet, a music player, a handledgaming console, a global positioning system (GPS) receiver, and awearable computing device (e.g., eyeglasses, wrist watch, etc.), or inany other form factor. The mobile device 1800 in this example includesone or more central processing units (CPUs) 1840, one or more graphicprocessing units (GPUs) 1830, a display 1820, a memory 1860, acommunication platform 1810, such as a wireless communication module,storage 1890, and one or more input/output (I/O) devices 1850. Any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 1800.As shown in FIG. 18, a mobile operating system 1870, e.g., iOS, Android,Windows Phone, etc., and one or more applications 1880 may be loadedinto the memory 1860 from the storage 1890 in order to be executed bythe CPU 1840. The applications 1880 may include a browser or any othersuitable mobile apps for receiving and rendering content streams andadvertisements on the mobile device 1800. User interactions with thecontent streams may be achieved via the I/O devices 1850 and provided tosearch engine 130, the search term suggestion engine 140, and/or othercomponents of system 100, e.g., via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (e.g., search engine 130, the search term suggestion engine 140,and/or other components of system 100 described herein). The hardwareelements, operating systems and programming languages of such computersare conventional in nature, and it is presumed that those skilled in theart are adequately familiar therewith to adapt those technologies toenhance search term suggestion described herein. A computer with userinterface elements may be used to implement a personal computer (PC) orother input of work station or terminal device, although a computer mayalso act as a server if appropriately programmed. It is believed thatthose skilled in the art are familiar with the structure, programmingand general operation of such computer equipment and as a result thedrawings should be self-explanatory.

FIG. 19 depicts architecture of a computing device which can be used torealize a specialized system implementing the present teaching. Such aspecialized system incorporating the present teaching has a functionalblock diagram illustration of a hardware platform which includes userinterface elements. The computer may be a general purpose computer or aspecial purpose computer. Both can be used to implement a specializedsystem for the present teaching. This computer 1900 may be used toimplement any component of the enhanced search term suggestiontechniques, as described herein. For example, the search engine 130and/or the search term suggestion engine 140, etc., may be implementedon a computer such as computer 1900, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to the searchengine 130 and/or search term suggestion engine 140 may be implementedin a distributed fashion on a number of similar platforms, to distributethe processing load.

The computer 1900, for example, includes COM ports 1950 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1900 also includes a central processing unit (CPU) 1920, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1910,program storage and data storage of different forms, e.g., disk 1970,read only memory (ROM) 1930, or random access memory (RAM) 1940, forvarious data files to be processed and/or communicated by the computer,as well as possibly program instructions to be executed by the CPU. Thecomputer 1900 also includes an I/O component 1960, supportinginput/output flows between the computer and other components thereinsuch as user interface elements 1980. The computer 1900 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of enhancing ad serving and/or otherprocesses, as outlined above, may be embodied in programming. Programaspects of the technology may be thought of as “products” or “articlesof manufacture” typically in the form of executable code and/orassociated data that is carried on or embodied in a input of machinereadable medium. Tangible non-transitory “storage” input media includeany or all of the memory or other storage for the computers, processorsor the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of a search engine operator orother search engine 130 and/or search term suggestion engine 140 intothe hardware platform(s) of a computing environment or other systemimplementing a computing environment or similar functionalities inconnection with search engine 130 and/or search term suggestion engine140. Thus, another input of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to tangible “storage” media, termssuch as computer or machine “readable medium” refer to any medium thatparticipates in providing instructions to a processor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—e.g., an installation on an existing server. In addition,the enhanced ad serving based on user curated native ads as disclosedherein may be implemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. It is intended by the following claims toclaim any and all applications, modifications and variations that fallwithin the true scope of the present teachings.

We claim:
 1. A method, implemented on a machine having at least oneprocessor, storage, and a communication platform connected to a network,for providing enhanced search term suggestions, the method comprising:receiving a set of search terms entered by a user; detecting useractivities performed on the set of search terms, wherein the useractivities include a descending phase and an ascending phase after thedescending phase; identifying a misinput corresponding to a first searchterm to which the user applies one or more backspaces; identifying theascending phase after the descending phase; for each ascending searchterm entered during the ascending phase, determining a probability thatthe ascending search term is a correction to the misinput; selecting oneof the ascending search terms as the correction to the misinput based onthe probability for each of the ascending search terms; and storing apair of the selected ascending search term and the misinput with theprobability of the selected ascending search term for future use.
 2. Themethod of claim 1, wherein the pair is identified by identifying thefirst search term at a beginning of the descending phase and byidentifying the selected ascending search term in the ascending phase asa corresponding corrected term based on the identified first searchterm.
 3. The method of claim 2, wherein the selected ascending searchterm is identified as the corresponding corrected term by virtue of thefirst search term having a same length as the corresponding correctedterm.
 4. The method of claim 2, wherein the selected ascending searchterm is identified as the corresponding corrected term by virtue of thefirst search term matching the identified corrected term with respect tosemantic meaning.
 5. The method of claim 2, wherein the selectedascending search term is identified as the corresponding corrected termby virtue of a number of positions that separate the first search termfrom the identified corrected term being within a predeterminedthreshold value.
 6. The method of claim 1, wherein the pair isidentified by extracting a pair of n-grams from the first search termand the selected ascending search term.
 7. The method of claim 1,wherein the determination of the probability is based on a noisy channelmodel.
 8. The method of claim 1, further comprising: determining whetherthe probability has breached a predetermined threshold value; inresponse to the probability having breached the threshold value,replacing the first search term with the selected ascending search term;and determining search terms to be suggested to the user based on theselected ascending search term.
 9. The method of claim 1, wherein themisinput comprises a skipped, unnecessarily added, and/or wronglyinputted character of an intended search term.
 10. A system configuredfor providing enhanced search term suggestions, comprising: a searchterm processor configured to receive a set of search terms entered by auser; a search term misinput detector configured to detect useractivities performed on the set of search terms, wherein the useractivities include a descending phase and an ascending phase after thedescending phase; the search term misinput detector configured toidentify the ascending phase after the descending phase; an n-gramsextractor configured to identify a misinput corresponding to a firstsearch term to which the user applies one or more backspaces; an n-grammisinput probability analyzer configured to, for each ascending searchterm entered during the ascending phase, determine a probability thatthe ascending search term is a correction to the misinput; and then-gram misinput probability analyzer configured to select one of theascending search terms as the correction to the misinput based on theprobability for each of the ascending search terms, wherein the n-gramsextractor is further configured to store a pair of the selectedascending search term and the misinput with the probability of theselected ascending search term for future use.
 11. The system of claim10, wherein the search term misinput detector is configured such thatthe pair is identified by identifying the first search term at abeginning of the descending phase and by identifying the selectedascending search term in the ascending phase as a correspondingcorrected term based on the identified first search term.
 12. The systemof claim 11, wherein the selected ascending search term is identified asthe corresponding corrected term by virtue of the first search termhaving a same length as the corresponding corrected term.
 13. The systemof claim 11, wherein the selected ascending search term is identified asthe corresponding corrected term by virtue of the first search termmatching the identified corrected term with respect to semantic meaning.14. The system of claim 11, wherein the selected ascending search termis identified as the corresponding corrected term by virtue of a numberof positions that separate the first search term from the identifiedcorrected term being within a predetermined threshold value.
 15. Thesystem of claim 10, wherein then-grams extractor is configured such thatthe pair is identified by extracting a pair of n-grams from the firstsearch term and the selected ascending search term.
 16. The system ofclaim 10, wherein the n-gram misinput probability analyzer is configuredsuch that the determination of the probability is based on a noisychannel model.
 17. The system of claim 10, wherein the n-gram misinputprobability analyzer is further configured to determine whether theprobability has breached a predetermined threshold value; and whereinthe search term processor is further configured to: in response to theprobability having breached the threshold value, replace the firstsearch term with the selected ascending search term; and determinesearch terms to be suggested to the user based on the selected ascendingsearch term.
 18. A machine-readable, non-transitory and tangible mediumhaving data recorded thereon for providing enhanced search termsuggestions, the medium, when read by the machine, causes the machine toperform the following: receiving a set of search terms entered by auser; detecting user activities performed on the set of search terms,wherein the user activities include a descending phase and an ascendingphase after the descending phase; identifying a misinput correspondingto a first search term to which the user applies one or more backspaces;identifying the ascending phase after the descending phase; for eachascending search term entered during the ascending phase, determining aprobability that the ascending search term is a correction to themisinput; selecting one of the ascending search terms as the correctionto the misinput based on the probability for each of the ascendingsearch terms; and storing a pair of the selected ascending search termand the misinput with the probability of the selected ascending searchterm for future use.
 19. The machine-readable, non-transitory andtangible medium of claim 18, wherein the pair is identified byidentifying the first search term at a beginning of the descending phaseand by identifying the selected ascending search term in the ascendingphase as a corresponding corrected term based on the identified firstsearch term.
 20. The machine-readable, non-transitory and tangiblemedium of claim 18, wherein the selected ascending search term isidentified as the corresponding corrected term by virtue of the firstsearch term having a same length as the corresponding corrected term.21. The machine-readable, non-transitory and tangible medium of claim18, wherein the pair is identified by extracting a pair of n-grams fromthe first search term and the selected ascending search term.